• DocumentCode
    3241634
  • Title

    Convergence Properties of Particle Filter Algorithm

  • Author

    Qu, Yanwen ; Chen, Yi ; Yang, Jingyu

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing
  • fYear
    2008
  • fDate
    22-24 Oct. 2008
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The basic sampling importance resampling algorithm is the basic for improving particle filter methods which are widely utilized in optimal filtering problems. In our paper, we introduce a modified basic SIR algorithm and analyze the convergence property of the modified basic SIR algorithm. Furthermore, when the recursive time is finite and the forth-order moment of the interesting function w.r.t the posterior joint distribution of the extended state is exist, the sufficient condition for the basic particle filter estimation convergence almost surely to the optimal estimation is discussed.
  • Keywords
    importance sampling; particle filtering (numerical methods); convergence property; particle filter algorithm; posterior joint distribution; sampling importance resampling algorithm; Computer science; Convergence; Electronic mail; Filtering algorithms; Monte Carlo methods; Particle filters; Recursive estimation; Sampling methods; State estimation; Yttrium;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2008. CCPR '08. Chinese Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-2316-3
  • Type

    conf

  • DOI
    10.1109/CCPR.2008.14
  • Filename
    4662967